清华大学社会网络研究中心

On behalf of the Editorial Board, it is our privilege to present the first issue of the Journal of Social Computing, affectionately shortened JoSoCo. Computing concerns the intersection of social behavior and computational systems. Historically focused on recreating human social conventions and contexts through software and technology, we propose its expansion to the full interface between social interaction and computation.

JoSoCo features social computing work that integrates social data mining, predictive modeling, machine augmentation, and social scientific theorizing. Computational models and machines, which are built to enhance the social world, are typically instantiated with social behavior embedded in data. These models predict and simulate that data recreate environments with social institutions, such as rating systems that convey reputation and quality. Alternatively, researchers may seek to create new machines, platforms or predictions designed to disrupt, complement or short circuit, rather than substitute for existing behavior signals, thereby facilitating novel environments and self-discovery.

Some authors claim that big data represents the end of theory, but we argue that social theory constitutes a critical interface for researchers who seek to obtain new social computing knowledge and know-how. Big social data from the web and distributed sensors can be used to measure variables from theoretical models to test hypotheses. Confirmations strengthen theory; violations provoke change. With big social data, theories can begin with weak assumptions; mining social data provides signals for their development to build strong insights, as argued in James’ paper “Social Computing Unhinged” from this issue.

Conversely, social science theories provide guidance and expectations for mining big social data. Theoretically informed qualitative and quantitative social research—such as systematic observation and population-sampled surveys—can de-bias results from data obtained by convenience. Furthermore, social science theories inspire the expressive capacity and predictive power of models and machines—such as crowdsourcing environments and recommendation systems—that we build and extend with them. Models that tightly fit with data and machines or platforms that increase predicted interactions or generate social values suggest high-order confirmations of the hunches and hypotheses that inspired them. In expanding and, indeed, unhinging the definition of social computing, we further welcome and seek to catalyze work that brings social theory into conversation with computational theory, social models into conversation with computational models, and social data into conversation with computational networks and interactions, in the service of understanding, creating, and computing social goods; flourishing; and innovation.